Quantum Architecture Search via Deep Reinforcement Learning

Understand

Recent advances in quantum computing have drawn considerable attention to building realistic application for and using quantum computers.

• However, designing a suitable quantum circuit architecture requires expert knowledge.
• For example, it is non-trivial to design a quantum gate sequence for generating a particular quantum state with as fewer gates as possible.
• We propose a quantum architecture search framework with the power of deep reinforcement learning (DRL) to address this challenge.